In the business world, models such as B2B (Business-to-Business) and B2C (Business-to-Consumer), which define relationships between companies and their customers, have existed for years. However, in my opinion, the rise of artificial intelligence and automation creates space for a new model: Business-to-Machine (B2M).
B2M is occasionally mentioned under different names, but the definition and some of the concepts described below are entirely original, based on months of analysis and reflection. Until now, I have briefly introduced B2M during training sessions and webinars. Now, it’s time to expand on what is becoming our present reality!
Business-to-Machine (B2M) is a model in which companies direct their marketing, sales, and service activities toward autonomous systems, programs, or devices* – usually powered by artificial intelligence– that make purchases and decisions on behalf of people, companies, or even other machines.
*In this context, I will use the term machine customers. They are sometimes also referred to as custobots (from customer (ro)bots).
According to the definition above, the traditional “human customer” in B2M is replaced by AI-powered algorithms that independently analyze offers, select the best products, negotiate prices, and finalize transactions. Some companies will therefore need to adapt their sales and marketing strategies for this new type of recipient—a machine customer that operates under different principles than humans**.
**However, as human decision-making processes are increasingly transferred to algorithms and systems, will these differences in principles truly be significant?
B2M (Business-to-Machine) on the Horizon
The specifics of B2M are gradually becoming clearer as business operations evolve. Machines are already beginning to make certain purchasing decisions. For example:
- In logistics and manufacturing, automated systems order missing components based on inventory analysis.
- In e-commerce and retail, algorithms analyze offers, check prices, notify users of changes, and recommend products based on order history or previously viewed items.
- In finance and banking, AI analyzes risks and independently makes decisions regarding financial products, such as investment assets.
- Building management and smart home systems are also increasingly utilizing AI. For example, certain IoT devices autonomously order consumable materials.
According to Gartner (source), machines will be responsible for 22% of generated revenue by 2030. Additionally, executives consider 2030 a likely tipping point for machine customers. However, these estimates date back to 2022, and I believe these changes will occur even faster. This means that companies failing to adapt risk losing access to a massive market segment—machine customers who won’t be influenced by emotions or brand loyalty but rather by algorithmic parameters, often executed through agents acting on their behalf.
Some manufacturing companies are already automating inventory replenishment. Humans merely define the rules and oversee their implementation.
Evolution Towards the B2M Model
How did we arrive at the concept of machine customers? This shift results from several technological advancements that have shaped and are shaping our preferences and new habits. At the core of these changes lies artificial intelligence—its ability to “understand” various datasets and employ statistical-probabilistic algorithms. However, AI is not the only driving force. Let’s take it step by step…
AI algorithms are becoming increasingly proficient at analyzing data, predicting needs, and making decisions, often more efficiently than humans. Artificial intelligence can compare dozens of offers simultaneously and select those that, according to the algorithm, best meet specific criteria (though not always successfully, as outcomes depend on the type of AI and model; let’s not forget about hallucinations).
Another significant trend, currently somewhat overshadowed, is the Internet of Things (IoT). More and more devices are network-connected and can autonomously communicate with sales systems. For example, smart refrigerators can order missing groceries. Additionally, humanoid, “dog-like,” and self-driving robots—which I believe will soon become common in many households—will also have the capability to connect with various systems and the internet.
Ordering products via voice assistants is already possible, though their capabilities remain very limited.
An important component is also the approach of companies themselves toward process automation (RPA, short for Robotic Process Automation). More and more systems are being integrated, and APIs—the programming interfaces that enable data exchange between applications—have become essential for many modern solutions. API usage will likely be most effective in the context of agent-based systems, and in B2M, we will increasingly talk about API-first commerce. (Further down, I have written more about MCP, short for Model Context Protocol, which was introduced by Anthropic at the end of 2024 and may contribute to integrating LLMs, or large language models, with various services and applications.)
Interestingly, even systems without APIs can now be incorporated into automation scenarios, though this field is still in its early stages. It is worth mentioning specialized models (sometimes referred to as LAM, short for Large Actions Model, though this term is not yet fully established). These models enable programs to “click” through interfaces on behalf of users or gather data in other ways, potentially making them a universal solution.
The changes described above will also drive the evolution of system and website interfaces to make them as machine-readable as possible. They will either transition toward database-like structures—less user-friendly for humans but excellent for machines—or move toward greater simplicity and universality.
Self-promotion: My company, Oxido, can provide support in all these areas.
For those interested, I have compared MCP and LAM below:
Model Context Protocol (MCP)
Model Context Protocol (MCP) is an open protocol/standard introduced by Anthropic at the end of 2024. It defines how applications provide context to large language models (LLMs). MCP functions as a universal interface (similar to the USB standard, which supports numerous devices), offering a unified and simplified method for integrating AI models with various data sources and tools, such as websites, databases, file systems, code repositories, and cloud services.
The MCP architecture is based on a client-service model with a communication layer that enables secure, two-way real-time data exchange. This allows AI models to interact with external sources, enhancing the relevance and timeliness of their responses.
Large Action Models (LAM)
Large Action Models (LAMs) are artificial intelligence models designed to transform user intentions—or those of AI agents acting on behalf of users—into concrete actions within specific environments, such as web browsers.
LAMs are optimized for executing complex, autonomous operations in real-world applications, considering situational context and real-time data from external sources. As a result, LAMs can independently make decisions and achieve business and operational goals, leading to automation and reducing human involvement in processes.
It is worth noting that the Operator in ChatGPT operates based on a similar model referred to as a Computer-Using Agent (CUA).
On a side note, one of my predictions also involves a shift in business models on another level: operational efficiency. In my training sessions, I discuss the AI First approach, where artificial intelligence serves as the foundation for building processes.
I believe the development of the B2M model can be divided into three key phases:
- Human-controlled algorithms – an approach already quite common in some sectors. Purchasing decisions are made by humans who use AI-based assistants merely as support tools (decision executors). This does not introduce significant changes to the business model—companies still tailor their sales strategies to human expectations and rely on traditional marketing methods***.
- Machines collaborating with humans – in this case, purchasing decisions are made through human-AI cooperation, where AI has a significant influence on choices. There are already some early examples. For instance, the Deep Research mode in ChatGPT can provide specific product recommendations based on multiple criteria. AI analyzes online data, suggests the best options, and the human makes the final decision. What does this mean for businesses? A stronger focus on visibility in search results used by AI models (a slightly different approach to SEO) and modifications to website content based on how artificial intelligence processes information.
- Fully autonomous machines – the third phase, at the time of writing this article (March 2025), is still largely a vision for the future, though I believe it is not far off. I expect we will increasingly hear about businesses and consumers delegating decision-making to machines/AI, allowing algorithms to make independent purchasing decisions. What might this entail?
- Warehouse management systems autonomously ordering missing components based on consumption predictions. This is already beginning to happen.
- AI assistants independently placing orders based on order history, observed usage, or scheduled calendar events.
- Autonomous vehicles reserving parking spots and purchasing electricity for battery charging at the best available rate. While this may still seem like science fiction, it might not be for long.
***You might recall the Google Duplex demo when a voice assistant booked a hairdresser appointment (this happened in 2018!).
The pace of change related to B2M and, more broadly, AI-driven transformations will vary depending on the industry, regulations, and technology adoption. However, it is difficult to assume that marketing and sales will remain the same as they were in the 20th century.
One interesting AI-driven shopping enhancement could be visualization. I wrote about this in an article on deep fake.
Why Should Companies Adapt to B2M?
Unlike traditional customers (humans), machines:
- Do not make decisions emotionally (though they can simulate emotions) and rely primarily on data – they analyze product specifications, prices, reviews, and transaction history. They are task-focused. At the same time, just like humans, machines can be manipulated.
- Can operate fully automatically and make decisions instantly, without the need for human interaction. Whether their output is merely a list of recommendations or an actual purchase depends on how the algorithm is programmed.
- Have defined preferences, and their choices are optimized based on predetermined rules rather than momentary impulses or advertising suggestions. The source of these preferences could be aggregated knowledge about us (I recommend reading about the concept of the digital twin as, in a way, our alter ego) or directives set by humans.
Given the characteristics outlined above, at least some businesses will need to adjust their operations – both in marketing (where precise, machine/AI-readable information will matter) and in sales (where APIs, automated negotiations/orders, and offer transparency will become crucial).
Examples will help better understand the reasons why consciously incorporating the B2M model into a business strategy is worthwhile. In the next two articles, I will present a set of hopefully inspiring ideas on how the B2M model can work in practice:
- in sales;
- in marketing.
I will publish them in the coming weeks. To make sure you don’t miss anything, I invite you to subscribe to the newsletter.
Does B2M Mean the End of Traditional Sales?
Definitely not. While automation is playing an increasingly important role, people will still have a significant influence on key purchasing decisions, and B2B and B2C models will remain highly relevant (Business-to-Machine model will often complement or extend B2B or B2C activitie). The B2M model does not eliminate humans from purchasing processes but rather redefines the roles of businesses and customers.
The B2M model will coexist with traditional sales methods, though for some companies, it may become the dominant or even exclusive way of conducting business.
The autonomous variant of B2M (stage 3) will initially be applied to orders that are:
- safe – meaning those where an incorrect decision does not result in significant consequences.
- repetitive – where household or business supplies are consistently replenished with the same product. The only variables are price and delivery conditions, which determine where the order is placed.
This gradual evolution of B2M will provide some additional time to adapt to the changes. Once again, assuming future coexistence with “traditional” B2B and B2C models.
Data Over Emotions – Algorithmic Approach to Marketing
Advertisements are designed to attract attention, engage, and persuade people to make a purchase. But what if the recipient of these messages is not a human but a machine? Marketing in the B2M model will focus on delivering precise, comprehensible, and algorithm-optimized information. Key aspects will include:
- product and service specifications in structured formats, such as clearly formatted tables, JSON, or XML (Google Product Schema is already widely used).
- transparency – product descriptions will increasingly include precise details (e.g., “weight: 1.2 kg” instead of “ultralight design”).
- data standardization – using unified measurement units and classifications.
It is highly likely that new marketing tools or even entire systems will emerge to ensure the distribution of information among AI agents acting on behalf of customers.
Marketing in the B2M model will revolve around data management and automation. The speed of response to algorithmic needs will be crucial.
As the B2M model gains prominence, traditional marketing methods will also evolve—in fact, some may become even more significant. I have already mentioned SEO, which will play a bigger role as AI agents and tools like Perplexity and ChatGPT search online resources. At the same time, the importance of classic search engines may decline (or rather, they will transform), and the way we consume content online will change.
Marketing automation tools, websites, and CRM systems will also undergo significant evolution.
Sales in the B2M Model – The End of Relationships?
Sales in the Business-to-Machine model will likely differ significantly from traditional B2B and B2C methods. The sales process will be adapted to the logic of AI systems—from intelligent product catalogs accessible via API to automated negotiations and transaction execution. Machines will purchase faster, analyze more offers in a short time, and remain unaffected by emotions, making sales to them more predictable and efficient.
Let’s take a look at the table:
| B2B/B2C Sales | B2M Sales | |
|---|---|---|
| Decision Maker | Human | Algorithm, following human instructions |
| Decision-Making Process | Knowledge, experience, intuition, emotions, price, relationships, recommendations from others | Data availability and analysis, price, predictions, algorithms |
| Sales Channels | E-commerce, personal contact | API, automated purchasing systems |
| Building Relationships | Important for customer loyalty, repeat purchases, and recommendations | Less significant – “loyalty” is based on process efficiency and offer parameters |
| Duration of the Purchase Process | Typically minutes or hours, but sometimes years | Usually seconds |
The B2M model will drive the standardization of the sales process. Blockchain technology and smart contracts may play a significant role, ensuring transaction security (e.g., funds are held until delivery is confirmed or the service is completed). Additionally, the role of intermediaries may decrease, and in stable times, the popularity of just-in-time delivery or similar concepts may increase.
On a side note, I also have an interesting vision for the evolution of wages and compensation. I outlined it in bullet points in my ReMarkeble a few years ago, and it seems like the right time to organize my thoughts on this topic and perhaps share them.
Implementing B2M in a Company
Implementing the Business-to-Machine model in sales can either result from a gradual, almost unnoticed evolution or a strategic and conscious decision. Regardless of the approach, adapting both technological infrastructure and sales channels will be necessary. Let’s focus on what this process could look like if carried out deliberately.
The first step should be recognizing algorithmic sales as an integral part of the business strategy. As a result, companies should gradually adjust their offerings, keeping in mind that purchasing decisions will be made by algorithms driven by data rather than traditional factors like brand loyalty. It’s also worth considering what will motivate human buyers (who delegate purchases or at least research) to rely on algorithms.
A key role in this process is played by IT modernization and the development of API-first commerce, as fast and efficient communication with AI systems will be crucial. In practice, this means investing in system integration. Companies must also ensure that product data is clear, structured, and accurate across all channels, aligning with algorithms such as those used by AI-driven search engines. Effectively addressing these tasks may require hiring specialists in artificial intelligence or data management. Marketers might also need to acquire more technical knowledge.
The implementation of B2M also requires securing systems against cyber threats and staying updated on evolving regulations governing AI-driven commerce—such regulations are likely to emerge in the near future.
The B2M model may create significant opportunities for blockchain technology and smart contracts.
If my predictions are correct, companies that start adapting their processes to B2M now may gain a competitive advantage in the coming decade when automated purchasing systems become the standard in many industries.
We Should Have Started Here, i.e., “Who Needs This?”
Let’s change our perspective and look at the future through the eyes of buyers. I believe that people will delegate more and more purchasing decisions to machines and algorithms, primarily for the sake of time savings and convenience.
In daily life, managing purchases—from groceries to service—requires attention and numerous small decisions. Assigning this task to intelligent systems will (in theory) allow us to focus on more important matters. Another motivation will be precision and rationality in decision-making. We often buy on impulse or under the influence of advertising—relying more on data for purchasing decisions should help.
Let’s not forget that corporations gather vast amounts of information about us. This means—sticking to the positives for now—that systems can select products that match our lifestyle, preferences, and budget, eliminating the need for us to define requirements in detail. Machine proactivity will be another advantage. Today, we forget to replace air purifier filters or order descaling solutions for coffee machines. Tomorrow, AI-powered devices will take care of it for us.
I’ll return to this topic later agian in the moment…
Companies will also benefit from automated purchasing, eliminating the need for manual inventory monitoring and ensuring timely orders. Systems will account not only for unit costs but also for projected consumption, delivery times, and stock shortages. As a result, AI and AI-driven machines will take over not only repetitive transactions but also negotiations and the selection of the best offers, leading to cost savings and greater operational efficiency.
I hope that automated purchasing will free up time for more important things. At the same time, I can’t shake the feeling that it always turns out the other way around…
I feel the need to briefly address the impact of collected data on purchasing decisions. Smartphones, loyalty apps, and online transactions allow companies to profile us precisely. Combined with AI, predictive models will not only recommend purchases but also make decisions for us—based on our habits, preferences, calendar events, and even account balance. This could be a great convenience but also a huge risk—for privacy, finances, and human relationships. Relationships again? Yes, because will you truly appreciate flowers if you know they were ordered by artificial intelligence, and the person handing them over is merely… well, handing them over?
Ultimately, I believe that many people and companies will change the way they handle certain purchases because we have grown accustomed to the convenience and personalization that technology offers. Since smartphones already suggest where to eat and how to take care of our health, it seems natural that AI will eventually take over the purchasing process as well. But I emphasize—this change will apply to only some purchases, and which ones will be a personal decision for each of us.
A Few Final Words
The Business-to-Machine model will not replace B2B or B2C. Instead, it represents another stage in the evolution of commerce and marketing in the AI era. It will bring extensive automation, requiring process optimization and system integration. Additionally, security—both for systems and transactions—will become even more crucial.
Despite these challenges, the B2M model has the potential to streamline various business sectors and help many quality-driven companies gain a competitive edge purely through product specifications, pricing, and efficiency.
I invite you to subscribe to the newsletter. In the coming weeks, two separate articles will be published, exploring sales and marketing examples aligned with the B2M approach. This way, you won’t miss these insights or future updates.
The B2M Model in Questions and Answers
How does B2M differ from traditional B2B and B2C models?
The B2B (Business-to-Business) model is based on transactions between companies, while B2C (Business-to-Consumer) focuses on sales to individual customers. In B2M (Business-to-Machine), purchasing decisions are made by autonomous systems, algorithms, and devices supported by AI – either instead of or on behalf of humans. This means that companies must adapt their marketing and sales strategies to machine-customers, which rely on data and algorithms rather than emotions or human relationships.
What technologies drive the development of the B2M model?
The concept of the B2M model has emerged thanks to significant technological advancements in recent years. Below, I have listed the key technologies and briefly described their role in the context of B2M.
- Artificial Intelligence – algorithms analyze data, optimize choices, and make purchasing decisions. AI agents, in particular, play a crucial role in this regard.
- Internet of Things (IoT) – devices connect to the internet, allowing them to autonomously order products and services based on identified needs.
- API-first commerce – companies are likely adapting (or will adapt) their sales systems to communicate with machine-customers through APIs. The MCP (Model Context Protocol) standard may gain popularity.
- Blockchain and smart contracts – enable transparent, secure transactions between AI systems.
- Web technologies and a new approach to UX and SEO – increasing alignment with the needs of AI tools and the way they consume content.
Does B2M mean the end of traditional sales?
No, B2M will not replace traditional B2B and B2C models. Instead, it will complement and expand them. Companies will continue to sell to businesses and retail customers, but the growing number of autonomous machine customers will make it necessary to adapt purchasing and sales processes to the new reality.
What steps can companies take now to prepare for the growing importance of custobots?
To adapt to B2M, companies should:
- Recognize machine-customers as an important part of their strategy and gradually adjust their offerings.
- Modernize IT and invest in API-first commerce to enable automated transactions and communication with AI systems; leverage the potential of MCP or similar technologies. This may involve hiring specialists in artificial intelligence or system integration and/or providing training. (By the way, check out the AI Training section.)
- Optimize and structure product data – create clear, algorithm-friendly specifications and metadata. Additionally, ensure data accuracy and provide fast access across all channels.
- Enhance websites to facilitate easy navigation for AI bots and adopt a slightly different SEO approach to ensure products are easily discoverable by AI-driven systems and search engines.
- Analyze machine-customer behavior and tailor offerings to algorithmic preferences.
- Secure systems against cyber threats and prevent manipulation in purchasing processes.
- Monitor regulatory changes.
Self-promotion: my company Oxido can help you with these areas.
What are the main challenges associated with implementing the B2M model?
Implementing the B2M model comes with several challenges:
- High technological costs – companies must invest in API development, system integration, AI, and the adaptation of sales platforms and websites. (Check out the article on AI implementation.)
- Lack of unified standards – different platforms and AI systems often use varying data formats and communication methods. The MCP standard or LAMs may provide a solution.
- Cybersecurity – autonomous purchasing systems may become targets for hacking attacks.
- Data transparency – companies need to understand how AI makes purchasing decisions and adjust their strategies accordingly.
What benefits will encourage retail customers to entrust their purchases to devices and algorithms?
Retail customers will be enticed primarily by promises of convenience and time savings. AI will handle repetitive purchases and device maintenance, eliminating the need to remember these tasks. Algorithms will also be promoted with the argument that their decisions will be more rational, selecting the best offers and helping retail customers avoid impulsive purchases.
What benefits will encourage business customers to entrust purchasing to AI?
Thanks to B2M, companies purchasing through this channel will benefit from automation and cost optimization. For example, AI will monitor inventory, analyze raw material consumption, and place orders at the perfect moment, minimizing downtime and reducing excessive stockpiling. Additionally, I assume that algorithms will be able to negotiate commercial terms, respond to dynamic price changes, and take advantage of short-term opportunities. Ordering office supplies will also become a thing of the past.
